Bot de Arbitraje Estadístico y Pares Cointegrados

El trading de pares es una estrategia cuantitativa clásica de retorno absoluto. Al identificar dos activos con relación estacionaria a largo plazo (e.g. Visa vs Mastercard, Chevron vs Exxon), el algoritmo vende el activo sobrecomprado y compra el sobrevendido cuando el spread se aleja 2 desviaciones típicas de su media histórica.

Nicho de Clientes Objetivo (Perfil de Cliente Ideal):

• Family investment offices seeking market-neutral hedge fund strategies
• Equities swing traders wanting to eliminate broad market directional risk
• Crypto quants trading Layer-1 correlation divergences (e.g. SOL vs AVAX)
• Independent wealth managers looking for non-correlated alternative assets

Price Series of Asset A & Asset B
   ↓
Engle-Granger Cointegration Test (Verify Stationarity of Residuals)
   ↓
Dynamic Kalman Filter (Calculates Real-Time Hedge Ratio Beta)
   ↓
Compute Z-Score of Spread
   ↓ (|Z-Score| > 2.0)
Simultaneous Execution: Long Undervalued / Short Overvalued
   ↓ (Z-Score Reverts to Mean 0.0)
Close Both Positions for Market-Neutral Profit

Subject: Market-neutral returns: Statistical Arbitrage for [Fund Name]

Dear [Portfolio Manager Name],

With macroeconomic uncertainty and interest rate volatility clouding market direction, having 100% directional equity exposure creates unwanted portfolio beta.

We deploy statistical arbitrage pairs trading systems. By identifying cointegrated asset pairs (such as payments or energy sector leaders) and dynamically hedging with Kalman filters, our strategies generate steady, market-neutral alpha with near-zero correlation to the S&P 500.

Our multi-pair equities portfolio achieved a 1.94 Sharpe ratio with a maximum drawdown of only 4.8% over the past 2 years.

May I send our complete quantitative research tearsheet and backtest data?

Sincerely,
[Your Name]